The Role of Market and Regulatory Discipline in Mortgage Lender

The Role of Market and Regulatory Discipline in Mortgage Lender Failures:
Bank versus Non-bank Failures in the Subprime Crisis
Zsuzsa R. Huszár * and Wei Yu †
Very preliminary: Please do not quote without permission
First draft: March 2011
Abstract
We provide a unique analysis of the failures of non-bank lenders in comparison with bank
lenders to examine the role of market and regulatory discipline in the subprime mortgage
crisis. We find that for non-bank lenders, the market discipline instead of promoting market
stability rather impair it. First, lenders with high concentration of refinance and minority
loans are more prone to fail despite that the information on these potentially aggressive
lending practices has been public for years prior to the crisis. Second, competition increases
risk taking and the probability of failures because lenders in competitive environment with
finite number of qualified borrowers likely increase lending to less qualified borrowers to
maximize revenues. More importantly, the similarities in bank and non-bank lenders’ failures
cast doubt on the importance of bank regulatory disciplines. Focusing on the bank lender
subsample, we find that regulatory oversight, such as the capital reserve requirements and
loan reserve ratios were generally inadequate. Since the higher loan reserves are associated
with higher probability of failures, banks likely took on risky loans for higher profits while
increased reserves which provided insufficient protection. Overall, we find that liquid asset
ratios had the greatest economic impact in reducing the probability of failures suggesting that
in addition to strengthening the existing regulatory requirements more emphasis should be
put on alternative risk management tools such as liquid asset ratios.
Keywords: non-bank failures, bank failures, mortgage market, subprime lenders, market
discipline, banking regulation.
*
Zsuzsa R. Huszár is from the Finance Department at the National University of Singapore (NUS). Mochtar
Riady Building, 15 Kent Ridge Drive, Singapore 119245. Huszár is also affiliated with the Risk Management
Institute (RMI) and the Institute of Real Estate Studies (IRES) at NUS. Email: bizhzr@nus.edu.sg. Tel: +(65)
6516 8017, Fax: +(65) 6779 2083
†
Wei Yu is from the Finance, Real Estate and Law Department at the College of Business Administration,
California State Polytechnic University, Pomona, CA. 3801 West Temple Avenue, Pomona, California 91768.
Email: weiyu@csupomona.edu
The authors gratefully acknowledge the comments of Sumit Agarwal, David C. Ling, and Alexander
Ljungqvist, Wenlan Qian and Weina Zhang.
In Watters vs. Wachovia, the Court ruled that the Office of the Comptroller of the Currency
(“OCC”) is permitted exclusively to regulate mortgage companies that operate as subsidiaries
of federally-chartered banks. In recognizing the OCC’s exclusive regulatory authority, the
Court’s decision effectively strips state regulators of their ability to enforce existing consumer
protection laws and hold bank mortgage lenders to the same standards for licensing, education,
and criminal background checks as mortgage brokers and other state-chartered entities.
US Supreme Court: Vol. 550. Docket No. 05-1342.
1. Introduction
Banking crisis, clustered bank failures, periodically occur all over the world where a
handful of financial institutions emerge as phoenix from the ashes and benefit by overtaking the
weaker ones. In the 1980s, during the Saving and Loan Association (S&L) crisis, many heavily
regulated financial institutions failed as they lost competitive advantage to commercial banks
while commercial banks gained the market shares of thrifts. Despite many differences between
the S&L and the recent crises, two important similarities are the overexposure to real estate and
the near collapse of an industry segment. In the 1980s, thrifts almost disappeared, while in the
recent crisis, non-bank subprime mortgage lenders and mortgage brokers seem to wane.
Among others, Mian and Sufi (2009) and Mian, Sufi, and Trebbi (2010) examine the
causes of the recent financial crisis, such as disintermediation, securitization, and the cooling of
the real estate market. They provide important insights about the geographic distribution of the
crisis by examining the geographic variation in the real estate price run-up and credit expansion,
but provide limited empirical insight about the cross-sectional differences in performance among
the different types of lenders. Some banks, such as Bank of America and Wells Fargo fared well
enough to benefit via “successful” acquisitions, while others have been acquired or are still in the
recovery process. 1 Similar situation can be observed in Australia, where the four big commercial
1
Belratti and Stulz (2010) examine the cross-sectional variation in bank performance to reveal why some bank
performed well in the crisis.
2
banks significantly increased their market shares as risk-averse clients moved back from non-
bank lenders to the safety of the Big Four despite higher interest rates.
Another strand of studies examine bank failures with the objective to provide guidelines
for establishing better early warning systems about bank failures (especially clustered events)
and aid regulatory supervision. Cole and Wu (2010) propose a simple dynamic hazard model
with time-varying covariates as a bank failure early warning model, by showing that the model
significantly outperforms regulatory probit models with and without the macroeconomic
variables. Boyd, De Nicoló, and Jalal (2010), testing Boyd, De Nicoló, and Jalal’s (2009)
banking model, find that with increased competition the probability of bank failures decline, but
warn regulators not to over-emphasize competition as a self-regulatory tool because competition
tends to increase loan-to-asset ratios.
As the media and academics focus on the commercial bank failures, the failures of
hundreds (if not thousands) of smaller non-bank lenders went relatively un-noticed after the big
failures Ameriquest Mortgage Co, and New Century Financial Corp. 2 But, the role of non-bank
lenders cannot be ignored as they provided a major portion of the subprime, now troubled,
loans. 3 These non-bank lenders are especially important because with the use of mortgage
brokers they heavily promoted disintermediation and potentially contributed to the loosening of
credit standards. Also, these lenders, without deposit base as a source of founding, heavily relied
on securitizations by non Government Sponsored Enterprises (GSEs) and involved other
financial institutions (e.g., investment banks) in their lending business. According to Iyer and
2
In general, we refer to non-bank lenders as non-depository institutions, financial institutions that are not under the
regulatory oversight of FDIC.
3
These two subprime lenders originated more than 160 billion in loans in a couple of year prior to 2008. The two
other important subprime lenders, Countrywide and Indymac can be considered banks as they operate banking
units and engage in deposit taking with FDIC guarantee.
3
Peydró (2010) these links between the affected financial institutions were likely crucial in the
spread of the crisis.
In this study, we aim to contribute to the literature on bank failures and provide the first
comprehensive analysis of non-bank mortgage lenders failures in comparison with bank failures.
Specifically, we examine the role of market and regulatory discipline in conjunction with lending
practices in explaining the recent large number of lender failures. Generally, in the unavailability
of financial data on non-bank lenders hinders a comprehensive analysis of non-bank mortgage
lenders. We address this issue, using lending characteristics (i.e., geographical concentration,
pricing, and securitization) and loan issuance (market size measure) information from the Home
Mortgage Disclosure Act (HMDA) raw Loan Application Register (LAR) file.
It is important to note that the recent financial crisis is term of time frame is quite
different than the S&L crisis, where institutions failed over a course of several years. In our
study, since the high concentration of lender failures around 2009 does not support any duration
or risk hazard model, we use standard logistics model in predicting lender failures. The
governmental intervention, via bad loan purchase and interest rate reductions, also contaminate
the data, making it uninformative to distinguish between two failures: the one in June 2009 from
the one March 2010. Thus, we treat all failures in the recent crisis as one event after 2007, and
use public information (on lending practices and from financial statements) from 2003 to 2007 in
the models.
Our findings suggest that lending practices play a key role in lender failure. Interestingly,
despite the significant differences in institutional details and regulatory requirements, bank and
non-bank lenders are driven by the same factors. For both types of lenders, higher concentration
of high credit quality borrower and low average loan amount are associated with significantly
4
lower probability of failure. In case of non-bank lenders, competition and transparency are
expected to aid the self-regulation of the new industry but proved to be harmful instead. The
higher concentration of competing lenders in the area is associated with higher probability of
failure. Possibly some lenders relax their lending standards to attract enough borrowers for their
business survival in strong competitive environment where the limited number of qualified
borrowers do not provide enough revenues for all lenders. Transparency on lending practices is
found to be under-utilized, as the evidence of aggressive lending to minorities already surfaced
before the crises, which could have been used to identify problem institutions well in advance.
Concerning bank lenders, we do not find evidence in support of the beneficial roles of
regulations. Regulation, instead of being useful, tends to be rather harmful as the higher
concentration of minority borrowers and high loan-to-income loans significantly increases the
probability of failure. The high concentration of minority loans may have been the result of
banks’ attempt to support under-represented borrowers and areas according to the 1977
Community Reinvestment Act (CRA) and the Clinton and the G.W. Bush Administrations’
National Homeownership Strategy. Bank regulatory requirements, such as capital and loan
reserves, are generally uninformative in explaining the failures. Interestingly, the probability of
bank failures increased with the loan reserve ratios suggesting that banks knowingly accumulated
risk but the regulatory cushion was insufficient.
Overall, we show that at the institution level, the failures of bank and non-bank lenders in
the recent financial crisis can be explained by similar factors, the low concentration of good
credit quality borrowers, and the high concentration of minority borrowers and refinance loans.
Neither the open market competition for non-bank lenders, nor bank regulations for bank lenders
were found to facilitate survival. Moreover, the risk management tools at the bank level rather
5
encouraged risk taking because banks were more likely to issue large quantity of risky loans
while accumulating loan reserves on the side, perceiving the risk to be insured. Our finding that
the liquid asset ratio is the most efficient tool in lowering risk of insolvency, suggests that
regulators may need to review existing regulatory risk management tools and focus on new
measures instead of strengthening the older ones.
2. Background
2. 1 Recent Development of the US Subprime Mortgage Market
Since the mid 1990s, subprime lending became a rapidly growing segment of the
mortgage market because it provided opportunities for homeownership to those who previously
could not qualify due to discrimination or due to a record of poor credit history. However, this
expanded access to mortgage loans came with a higher price (Fortowsky and LaCour-Little,
2002; Brent, 2007), thus making subprime lending also known as high-cost lending (Pennington-
Cross 2006). 4 Today, many argue that regulatory changes resulting in loosening of regulations
played an important role in the development and the collapse of the market (Mian and Sufi,
2008). For example, the 1982 Alternative Mortgage Transaction Parity Act (AMTPA) allowed
the introduction of adjustable-rate mortgages, balloon payments, and interest-only mortgages.
The 1986 Tax Reform Act (TRA) allowed interest deductions on mortgages and thus made
mortgage debt cheaper to consumers and also encouraged homeowners to do cash-out
refinancing by renewing their loans (Pennington-Cross 2006). Lastly, the 1998 Homeowners
Protection Act (HPA) required the homebuyers with loans higher than 80% loan-to-value ratio to
pay for extra insurance to reduce the risk of the loan.
4
For detailed review on the evaluation of the US subprime mortgage market read Courchane, Surette, and Zorn,
(2004) and Chomsisengphet and Pennington-Cross (2006).
6
In the new millennium, the booming economy and the increasing real estate prices
created increasing demand that the lenders were more than ready to meet, especially as the
relaxed regulations allowed them to charge higher rates and fees at lower cost with
disintermediation, with reliance on mortgage brokerages. In addition, the lenders seemingly
passed off most of the risk by selling off loans in the secondary market and continued to leverage
their positions and “pump” credit into the market. Although in hindsight it is hard to argue in
support of the subprime mortgage market, it did provide more than 5 million home purchase
subprime loans, including more than 1 million loans first time home purchases (mostly to
previously under represented borrowers). But total of about 10 million new subprime loans
included loans for refinancing, investments and speculation that are difficult to view in positive
lights (Jaffe, 2008).
2.2 Mortgage Market Participants and Regulations
Recently, the retail mortgage market became increasingly heterogeneous. New financial
institutions, such as mortgage companies and finance companies entered the market, besides the
traditional bank lenders such as commercial banks, saving and loan associations, and credit
unions. Banks are required to fulfill Basel II requirements, and annually evaluated based on
CAMELS criteria, as well as CRA guidelines. 5 The non-bank lenders, finance companies,
mortgage companies, and merchant banks, subsidiaries of industrial firms and/or foreign banks,
are less regulated. In addition, these lenders are also relatively opaque because their financial
5
According to CRA, commercial banks and savings associations are expected to meet the needs of borrowers in all
segments of their communities, including low- and moderate-income neighborhoods. The objective of CRA is to
reduce discriminatory lending practices against low-income neighborhoods, known as redlining.
7
information is not readily available in the market, making it difficult for lenders and investors to
understand the risk these financial institutions were taking. 6
Despite the economic importance of these non-bank lenders in the US market, there is
limited research on the lending practices of these financial institutions. One notable exception is
Ambrose’s (2003) study, which compares bank and non-bank lenders in the commercial
mortgage market and shows theoretically and empirically that bank and non-bank lenders use
different lending strategies. While banks use interest rates in allocating credit, non-banks adjust
credit allocation with the use of the loan-to-value ratio. Therefore, the borrower clientele is likely
to be different for those of banks and non-banks, because clients with a need for large loans may
incur the higher rates with a bank loan. These findings are consistent with that of the US, UK and
Australian residential mortgage market prior to 2007, where borrowers in search of lower rates
(at least lower initial rates) chose non-bank lenders.
Bank lenders are regulated at the state or the federal level and had to follow international
rules while the non-bank lenders were mostly self-regulated. In a perfectly competitive and
transparent market environment, competition would eliminate the bad lenders that aggressively
target protected borrowers and engage in unethical business practices. 7 Despite high degree of
transparency ensured by the equal lending opportunities act and HMDA, the information on
lending practices was neither available timely nor well organized. Thus, market discipline via
investors monitoring and competition was ineffective. Until today, the lending practices of the
different subgroups of lenders, such as investment banks, mortgage companies, finance
companies, thrifts and banks are relatively unexplored. The likely reason is the limited financial
6
In the aftermath of the crisis, the obtaining reliable financial information on the non-bank, mostly subprime
lenders, is a challenge.
7
Protected Borrowers are minorities and/or low-income borrowers who are classified as protected borrowers under
the Fair Housing Act (Federal Reserve, 2006) and were found to be especially targeted by aggressive lenders,
because of the generally lower level of financial education (Hill and Kozup, 2007)
8
information at the institution level for the non-public lenders, at least in the US market, hinders
comprehensive empirical studies.
2.3 Bank failures
Despite the relative infrequency of bank crises around a world, a significant research has
focused on the topic because of the macro economical importance of this sector. Especially, the
spread of bank failures, so called contagion effect, concerns regulators. Initially two competing
hypotheses emerged: pure panic and information-based contagion. Aharony and Swary (1996) is
study provides additional evidence consistent with the latter hypothesis. More recently, Iyer and
Peydró (2010) further explore the informational hypothesis and reveal that interbank linkages
with failed bank are important source of contagion especially involving weak banks. They
suggest that regulators pay more attention to interbank links in the future to reduce the contagion
effect, and to evade similar situation as in the recent crisis.
Interestingly, the role of market discipline, as monitory tool has attracted much attention
at the end of 1980s, when the FDIC proposed the use of subordinated notes and debentures
(SND) as a way of increase market discipline and augment regulatory discipline on banks and
banking organizations. Avery, Belton and Goldberg (1988) find that the potential for market
discipline is weak at best, as the pricing signals that bankers receive from the public subordinated
debt market appear to be at odds with the directions desired by regulators. While Hannan and
Hanweck (1988) find that the market prices the risk as the CD rates are higher for banks with
more variable income.
Another stand of studies focuses on establishing early warning system for regulators.
Whalen (1991) propose a proportional hazard model of bank failures using bank data including
170 failures from 1985 to 1990. Using typical financial ratios and local economic condition
9
measures his model identifies both failed and non-failed banks with high accuracy. Cole and
Gunther (1995) use a split-population survival-time model to distinguish factors that result in the
ultimate failure from those that influence the survival time of failing banks. A few measures of
bank’s financial condition, such as capital reserve, troubled assets, and net income, are important
in explaining the timing of bank failure, while other variables in bank failure models, such as
measures of bank liquidity, are not associated with the time to failure. More recently, Cole and
Wu (2010) introduce a dynamic hazard model with time-varying covariates as an early warning
model for bank failures, which significantly outperform simple probit models (often used by
regulators) with and without the macroeconomic variables.
2.4 The Fall of Retail Mortgage Lenders: Regulation, Transparency, and Competition
While there is significant heterogeneity across national bank crises, the main causes are
high credit growth, a negative GDP growth and a high real interest rate. In the recent financial crisis,
the triggers were the high credit growth and the increasing interest rates in conjunction with the
cooling of the real estate market. In 2006-2007 the “surprising” cooling of the residential house
market and the increase in interest rates made refinancing more difficult, while the negative
equity loans encouraged even non-financially constrained borrows to strategically default on
their loans. For the over-leveraged mortgage companies that heavily relied on external funding
(e.g., Asset backed commercial paper market), the soaring mortgage delinquencies quickly
resulted in liquidity problems.
As the quality of mortgage backed securities declined, the asset backed commercial paper
market dried up and making the issuance of new securitized products difficult thereby cutting
funding for non-bank lenders. The financing difficulty and the short term liquidity problems
were crucial for many lenders that could not cope with these difficulties and failed. While the
10
Fed and the Treasury with the support of the Administration made attempts to stabilize the
financial market after the critical 2008 failures (e.g., Lehman Brother and Bearn Stern), there
was too much focus on the surface liquidity. Regulators have not realized that banks will be less
willing to lend to each other and the 2008 and 2009 stimulus bills were unsuccessful in
tempering the general economic downturn.
3. Hypotheses
In the recent financial crisis, a system wide shock affected countless financial institutions,
commercial banks, non-bank lenders. Insurance companies, including investment banks were
also affected because of their indirect involvement in the subprime mortgage market via
securitized investment products. For banks, capital reserve ratios and CAMELS ratings should
have provided warning signs about the overexposure to real estate market risk, and the potential
risk for bank failure, while for non-banks, the open market competition was expected to resolve
any inefficiency with the help of transparency.
This study aims to contribute to existing bank failure literature by examining the factors
that contributed to the failure of lenders (bank and non-banks) during and after the subprime
crisis. More specifically, we categorize our lenders into two groups: (1) Bank and (2) Non-bank
lenders, where banks are primarily defined as depository institutions. The comprehensive
analysis of the two different types of lenders in the recent mortgage crisis allows us to answer
various important research questions.
First, we focus on non-bank lenders, where in the absence of financial information we
can examine only the role of aggressive lending, competition and transparency in failures with
lending information. We examine the relationship between aggressive lending, bank
11
concentration and probability of failure, to shed light on the efficacy of self regulation, with the
following two hypotheses
H1A: The probability of non-bank failure is higher in competitive environment
H1B: The probability of non-bank failure is higher with higher concentration of minority
and refinance loans.
If self-regulation is effective, banks in more competitive environment are less likely to
fail. Earlier studies find that subprime lending concentrates in low income and minority
neighborhoods (Calem, Gillen, Wachter, 2004) where the potentially high cost loans are not
necessarily aiding the revitalization of the neighborhoods. A more recent study by Williams and
Bond (2007) shows that new subprime loans from traditional lenders had some positive effect in
decreasing segregation, but the loans from new subprime lenders, often specializing in minority
and low income borrowers rather increased segregation. Thus, the subprime lenders did not seem
to achieve the social benefit that regulators hoped they would. Since the information is public on
minority and low income borrower concentration, in an efficient market, the past public
information should be reflected in the value of the institution (especially for public firms) which
is unlikely to predict failures.
Second, we examine the role of regulations by comparing the failures of banks and non-
banks. If regulations to some extent were efficient then the role of aggressive lending in bank
failures should be less pronounced.
H2: The relationship between failure and aggressive lending proxies, such as the
concentration in minority and low income borrowers is economically less important for
banks because of banking regulations (besides the market discipline).
12
But, even if we find that the banks are also more likely to collapse when they have high
concentration of minority and low income borrowers, we still cannot conclude that these
institutions are actively engaged in aggressive lending. Unlike in the case of non-banks, banks
may have high concentration of the underrepresented risky borrower to encourage first time
home ownership in line with the Administration’s initiative.
Lastly, focusing on banks, the authorities, through the regulations on bank activities,
capital adequacy, annual supervision (Barth 2004), should have noted that banks excessively
increased their real estate risk. Thus, we examine the effectiveness by testing whether low capital
reserve or loan reserves were signaling future insolvency. Lastly, we examine whether liquidity
ratios were effective warning signals as some countries, in addition to Basel II requirements also
enforce higher liquid asset ratios to alleviate the risk of insolvency as a result of liquidity
problems which was critical in the recent crisis. 8
H3A: Capital reserve ratios and loan reserve ratios are insignificant in predicting
lender’s failure
H3B: High liquid asset ratios can significantly reduce the probability of bank failures (as
these ratios are in place in countries that have been affected by the 1997 Asian Financial
Crisis).
4. Data
4.1 Identification of Failed Mortgage lenders
8
For example, the banking regulation in Singapore requires the maintenance of up to 18% liquid asset ratio and a
minimum of 3% cash balance.
13
We collect information on all failed residential lenders from Jan 1, 2007 till 31 Oct 2010,
including banks and non-banks. Failed banks are primarily identified from the Federal Deposit
Insurance Corporation (FDIC) website under the section of ‘Failed Bank List’ for the
corresponding sample period of our research. 9 Information on failed non-banks lenders, such as
mortgage firms, or subsidiaries of investment companies is very restricted as these financial
institutions are not linked to FDIC, or OCC. 10 With web query and the use of ‘The Mortgage
Lender Implode-O-Meter’ website, we identified about 300 non-bank institutions with location. 11
Our research focuses on studying mortgage lending practices during the subprime crisis,
thus it is crucial that the failed financial institutions in our sample are actively involved in the
mortgage lending business. To identify the respective mortgage lenders, we decided to work with
the Home Mortgage Disclosure Act (HMDA) Lender File. Most lending institutions with offices
in metropolitan areas are required by the Home Mortgage Disclosure Act of 1975 to disclose
information with regards to their lending activities. Annually, the HMDA data contains over
8,000 lenders, accounting for at least 80% of the loans extended in the U.S. (Avery 2005).
We match the hand collected default sample of 731 failed lenders (banks and non-bank
lenders) with the HMDA dataset by (1) Lender name, (2) City and (3) State, which results in a
match of 517 lenders with unique IDs. We include lenders that are among the 8,886 unique
lenders that reported to HMDA in 2006 before the onset of the crisis. We use manual name
matching and ensure that the failed institution was the same as that in HMDA by checking the
location variables. The allocation of a unique HMDA ID code also allows us to account for
overlaps that might occur, because of the double counting subsidiaries and the parent company
9
The Federal Deposit Insurance Corporation, FDIC.http://www.fdic.gov/(accessed Nov 5, 2010).
10
Interesting that one of the most organized sources of information about non-bank financial is available via Home
Mortgage Disclosure Act’s LAR database.
11
The Mortgage Lender Implode-O-Meter. http://ml-implode.com/index.html (accessed Nov 5, 2010).
14
(bank). In addition, we remove all foreign banks, or subsidiaries of foreign banks, but keep all
subsidiaries or American banks that did not fail, classifying these failures differently in the
empirical analysis.
The HMDA agency codes allow us to identify the regulatory oversights for the
institutions. All financial institutions which are regulated by the (1) Office of the Comptroller of
the Currency (OCC), (2) the Federal Reserve Board (FRB), (3) Federal Deposit Insurance
Corporation (FDIC) or the (4) Office of Thrift Supervision (OTS) are identified as banks. All
others which are not regulated by the aforementioned four authorities are identified as non-
banks. Overall we identify 517 failed lenders with valid lending information, including 344 bank
and 253 non-bank lenders. In addition, using the HMDA performance data, we also identify
2,240 bank lenders with valid HMDA and banking information and 3,215 non-banks with valid
HMDAID and lending information. Lastly, for banks (failed and non-failed bank lenders), we
obtain financial statement data from Bankscope to control the bank health and profitability.
4.2 Description of Variables
Using HMDA annual reports for each lender, we calculate a series of variables that
capture their exposure to the lending market to specific states and some variables that reflect
potentially aggressive lending behavior. We create a number of variables to capture lending
practices, such as total loan application, total loan origination, annual approval rates, and
concentration of refinance and repurchase loans. We attempt to measure the degree of
responsible lending practices with geographical loan concentration, minority loan concentration,
and the concentration of high-cost, high-rate, and HOEPA loans. 12 We also calculate the change
12
HOEPA loans are the loans that are required to be disclosed as high cost loans according to the Home Ownership
and Equity Protection Act Amendments (HOEPA). In general, first (and second) lien loans with rates 8 (10)
percentage point higher than that of comparable treasures are required to be disclosed.
15
in these variables over the period of 2003 to 2007, to capture whether lenders change their
lending practices in terms of becoming more (less) diversified or more (less) exposed to
minorities. Full list of lending practices variables, applicable to all bank and non-bank lenders, is
included in Panel A of Table 1.
In addition, we obtain financial information such as balance sheet and cash flow
statement for all banks through Bankscope. 13 Specifically, we collect information about the
bank’s asset, equity, liquid asset, off-balance-sheet items (OBS), net income, total deposits and
total loans. To assess banks financial stability, we obtain information on total capital and tier one
capital reserve ratio, total loan reserve, and loan to equity ratio, loan to earning asset ratio, and
total problem loans to liquid asset ratio. In addition to the annual averages, we also calculate
trends, that is, the change in deposit base, liquid assets among others. Change in the bank
financial status could provide important warning signal for future funding difficulty, or liquidity
problem, and for the ultimate failure. The complete list of Bankscope variables are described in
Panel B of Table 1.
[Table 1 about here]
5. Empirical Analysis
5.1 Summary Statistics
Panels A and B of Table 2 display the summary statistics for failed 249 bank and 329
non-bank lenders, respectively. The summary statistics suggest that failed bank and non-bank
residential mortgage lenders are comparable in their lending activities. Since both types of
lenders issue more refinance than purchase loans, the majority of loans was not to promote first
time homeownership as many regulators argued.
13
Bankscope is accessed via Wharton Research Data Service (WRDS).
16
About 22-24% of the borrowers from areas with high FICO scores while the average loan
amounts is about 2.2-2.8 times of the annual incomes. The somewhat higher loan-to-income
ratios for bank lenders suggest that banks are more inclined to issue loans to lower income areas,
or to lower income borrowers. One notable exception is the bank lenders’ relatively heavy
exposure to minority areas (51 versus 40%), potentially in response to governmental initiatives to
promote minorities housing. More interestingly, while the average number of applications is
significantly more (35 thousand versus 27 thousands) for banks than non-banks, the issued
amounts are relatively comparable (63 versus 53 million). Because non-banks lenders not only
have higher origination rates, 65% versus 50%, but also issue larger loans.
[Table 2 about here]
The time trends are also similar and consistent with the media reports following the
crisis. Both types of institutions steadily increased loan originations and focused more on
minority neighborhood. More importantly, the percentage borrowers from high credit quality
areas declined. Interestingly, the approval rates have also declined over time, likely as the result
of generally lower credit quality borrowers. For the still active bank and non-bank lenders, the
trends are quite different in Table 3. For these lenders, there is no significant increase in minority
borrowers or decline in the credit quality.
[Table 3 about here]
The lenders successfully surviving the crisis were generally less exposed to minorities
and lower income borrowers, reflected by the lower Pctminorty and Loan-to-income variables in
Panels A and B of Table 3. More importantly, these institutions are much less exposed to the
residential mortgage market reflected by the significantly lower application numbers and the
total issuance amount (Amount in Mill). For these institutions, the high origination rates are
17
unlikely to reflect aggressive lending or lower lending standards because of the high
concentration of loans in high credit quality areas (captured by high PctgoodFICO).
[Table 4 about here]
While for non-bank lenders we are unable to collect reliable financial information, for
bank lenders we are able to compare important bank risk management measures across failed and
non-failed bank lenders. Consistent with Beltratti and Stulz (2010), we show that the banks that
successfully survived were significantly less profitable from 2003 to 2007 in terms of net income
(NI) and net profits (Profit). But these profits were declining with the onset of the mortgage
crises and the declines in real estate prices. The over-exposure of these banks to the mortgage
market was quite clear, as the total problem loans relative to capital reserves (Tprobloans_cap)
and liquid asset (Tprobloans_liqAt) are about .23 and .47 for the failed institutions already before
2007. For the non-failed banks, the Tprobloans_cap and Tprobloans_liqAt measures are
negligible at about .03 and .19.
5.2 The Role of Lending Practices in Bank and Non-bank Lender Failures
At first glance, the logistic regression analyses for predicting the failure of mortgage
lenders does not reveal anything surprising. The probability of failure is negatively related with
higher concentration for borrowers from good FICO score areas and lower loan-to-income ratios.
The higher percentage of subprime highrate loans and refinance loans tend to increase the
probability of failure. For banks, the higher concentration of borrowers with minority areas is
detrimental, as a 1% increase in Pctminority increases the probability of failure by 1.8%. Banks
were strongly encouraged to promote minority homeownership; and thus, regulators and political
forces were likely partly responsible for the higher minority lending and the subsequent negative
consequences.
18
[Table 5 about here]
Since overall the coefficient estimates and the reported odds ratios are similar
(economically and statistically not significantly different), the question arises whether banking
regulations were at all significant. 14 We also examine one aspect of self regulation, competition,
and find that lenders in competitive environment are more likely to fail. The 1.214 odds ratio on
the HighComp variable for non-bank lenders suggest that lenders that on average operate in areas
where the completion was stronger than the median level of completion are about 20% more
likely to fail. Intuitively, in the presence of inelastic supply of good credit quality borrowers, the
stronger competition restricts some lenders to attract economically profitable number of new
borrowers with good credit quality. Thus, as lenders are more likely to relax lending standards
and increase risk taking in high competitive markets, the foundation of open economy and the
benefits of competitions are at question.
In Table 6, in addition to the past average lending practices, we also consider the role of
time trends. Specifically, we examine whether increased loan amount, loan origination and
approval rates that reflect relaxation in lending standards predict lenders failure. Although odds
ratios on the change in loan amount (Chngloanamount) and loan origination (Chngloanorig) are
positive and statistically significant, the economic significance is negligible in comparison with
the effect of the loan-to-income ratio or the concentration of good credit quality borrowers
(PctgoodFICO). But, the “abnormally” large positive odds ratio of the change in approval rates
variable (Chngpctapproval) clearly shows that most failures are concentrated among institutions
that significantly increased the approval rates from 2003 to 2007.
[Table 6 about here]
14
Beltratti and Stulz (2010) raise a similar question in examining the role of regulations across countries in
conjunction with bank performance. They find that on average bank regulatory indices are uncorrelated with bank
risk measures thereby casting serious doubt on the efficiency of banking regulations.
19
5.3 The Role of Financial Performance and Bank Management in Bank Failures
The comparable magnitude of bank and non-bank residential mortgage lenders’ failures
(about 300 in absolute and 10% in relative terms) suggest that regulations were ineffective. If
bank regulations were effective in reducing excessive risk taking or ensuring suitable capital
reserve, then bank failures should be less prevalent than non-bank failures. In addition, the
results in Tables 5 and 6 also suggest that similar lending practices were the driving forces of
bank and non-bank failures. But, based on these findings we cannot assertively conclude that
regulations were ineffective because we may have overlooked a number of non-bank failures.
To address the selection problem, we examine the role of regulations in a subsample of bank
lenders by focusing on specific regulatory requirements such as loan reserves and capital reserve
ratios.
[Table 7 about here]
In general, consistent with the results from Tables 5 and 6, Table 7 shows that banks are
less likely to fail with lower minority lending concentration, lower concentration of refinance
loans and lower loan-to-income ratios. More importantly, as we examine the role of financial
information, we find that traditional banking with higher deposit ratios (deposit relative to total
bank assets) significantly reduces the probability of failure (1% increase in deposits relative to
total assets reduces the probability of failure by 2.5%.). Naturally the higher percentage of
problem loans and off balance sheet items are associated with significantly higher default
probability.
Overall, considering the role of regulations, we find that although the higher capital
reserve ratio slightly reduces the probability of failure, the loan reserves and liquid asset reserves
are ineffective in evading failures. Especially the high odds ratios on the loan reserve ratios are
20
alarming because instead of providing insurance (security cushion) for loan losses, the loan
reserves seem to encourage even more risk taking. Putting our findings in the context of
proposed and new regulatory changes, we are suggesting more reliance on new measures, such
as increased liquidity. Liquidity ratios could be implemented in conjunction with different asset
and liability types. The traditional liquidity ratio, as percentage of deposits needs to be revised as
banks are less and less reliant on deposits.
In response to the crisis, President Barack Obama proposed drastic financial reforms that
manifested in 2010 as the Dodd–Frank Wall Street Reform and Consumer Protection Act.
Naturally, the Act emphasizes the role of regulations, especially aiming to out regulatory
oversight―consolidate bank regulatory agencies and create new oversight council to evaluate
systemic risk. In addition, the Act promotes increased transparency to identify risks at
institutional level which potentially may cause system wide shocks. However, the planned
increased oversight and transparency may not be beneficial without clear objectives; as already,
prior to the crisis, staggering amount of lending information was available in the system which
was hard to process.
5.4 Robustness Analysis
In robustness analysis, we have used alternative samples. First, we excluded Washington
Mutual and its subsidiaries and replicated the analysis from section 5. In addition, we also
considered alternative classification where a financial institution is considered depository
institution if the deposits account for non-negligible amount of the assets (using 5 and 8%
cutoffs). This latter robustness test is crucial to address misclassification issues, such as the case
of Indymac, where the lender is clearly a non-traditional subprime lender despite that it owns a
commercial bank and can take deposits.
21
6. Conclusion
Since 2008 hundreds of banks filed for bankruptcy, have been acquired, or merged with
FDIC assistance. In addition, hundreds of non-bank lenders (e.g., now infamous subprime
lenders such as New Century) have also failed resulting in a near collapse of the subprime
lending market segment. With the failures of these non-bank lending institutions, large
commercial banks continued to increase their market size and now a world-wide phenomenon is
that a handful of large institutions dominate the lending market. Examining the failures of non-
bank lenders in conjunction with bank failures provide a unique opportunity to shed light on the
effectiveness of market and regulatory disciplines in the mortgage market.
In this study, we aim to contribute to the literature on bank and non-bank lenders failures
by focusing on institutional details (e.g., bank management and lending practices) in explaining
the cross-sectional variation in lenders’ failures in the aftermath of the recent financial crisis.
Like other studies on the recent mortgage crisis, we are unable to compare the risk management
practices and financial information of bank and non-bank lenders in the absence of reliable
financial data for the non-bank lenders. To address this shortcoming, we use information on
mortgage lending practices from HMDA in our empirical analysis. Specifically, we focus on the
role of market discipline and aggressive lending practices in examining the non-bank failures. By
contrasting the role of potentially aggressive lending practices in bank versus non-bank lender
failures, we provide new insight about the role of regulatory discipline.
Our findings suggest that lending practices, such as sound lending standards, play a key
role in reducing the probability of lender failures. In general, the higher concentration of high
credit quality borrowers and low average loan amount are associated with lower probability of
22
failure. For non-banks we find that self-regulation through transparency and competition was not
only ineffective but rather harmful. First, we find that lenders overly exposed to refinance and
minority loans are more likely to fail. These strong relationships between the potentially
aggressive lending practices and failure are especially alarming since the public has been
informed about these lending practices for years prior to the crisis. Second, the competitive
environment did not promote better lending practices but rather more risk taking and increased
the probability of failures. The presence of high concentration of lenders, when the number of
qualified borrowers is limited, is likely to encourage lenders to relax lending standards in an
attempt to issue more loans for greater revenues.
Interestingly, despite the significant differences in institutional details and regulatory
requirements, the failures of bank and non-bank lenders are driven by similar factors. At the first
glance, the comparable failure rates across the bank and non-bank lenders already suggests that
bank regulations were inefficient. Focusing on the bank subsample in examining the role of
regulatory credit risk measures, the capital reserve requirement provides only weak protection,
while the loan reserve ratio is entirely ineffectively. The strong positive relationship between
failure and loan reserve suggests that banks knowingly take risk increase reserves but the higher
level of reserves are still insufficient. We find that the higher liquid asset ratios provided the best
tool to reduce the probability of failures. Thus, we recommend that regulators consider new
alternative risk measures instead of strengthening the relatively inefficient existing regulatory
requirements.
23
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26
Table 1. Variable descriptions
In general, all variables are calculated as the time series average of the annual number from 2003 to 2007. For
example, the application variable is average of the annual total applications from 2003 to 2007. The change variables
are calculated as the time series average of the annual changes to capture time trend. For example, the Chngamount is
the average in the five year annual changes in the total issued loan amount from 2003 to 2007.
Panel A. Lending variables based on HMDA dataset
Variable name Description of variables
Failure Dummy variable that takes on the value one for failed lenders (banks and non-banks) , the
lender is unable to operate any longer in the residential market.
Application Total number of loan applications
Originate Total number of originated loans
Pctoriginate Approval rate in percentage, approved loans relative to total loan application
Loan-to-income Average loan-to-income ratio, excluding high cost loans, such as HOEPA and spread-
reportable loans
Pctminority Percentage of total loan application by minorities
PctgoodFICO Percentage of total loan application with high credit score, as the percentage of loans from
census tracts where the median credit score is in the top quartile relative to total loan
applications
Avgamount Annual average of the approved loan amount
Amount Annual total loan amount issued by the lender
Pctpurchase Percentage of loan application for purchase purposes, as the ratio of purchase applications
relative to total loan applications
Pctrefinance Percentage of loans application for refinance purposes, as the ratio of refinance
applications relative to total loan applications
HHI Banks’ Herfindahl-Hirschman Index (HHI), which is the average of all census tracts,
where the lender has issued loans
High_HPI Dummy variable which takes on the value one if more than half of the originations are
in areas with high property prices. Areas with high property prices are identified as
those with HPIs from the top quartile.
Concentration Geographic lending concentration measure, calculated as sum of squares of the total
lending (number of loans) per state
Highrate Annual average of total number of spread reportable loans
HOEPA Annual average of total number of HOEPA loans
Chngamount Annual average of the increase (or decrease) in the total amount of loan issued from 2003 to
2007
Chngloanorig Annual average of the increase (or decrease) in the total number of loan originated from
2003 to 2007
Chngpctapproval Annual average of the increase (or decrease) in approval rates from 2003 to 2007
Chngpctminority Annual average percentage of loan application by minorities from 2003 to 2007
ChnggoodFICO Annual average change in the percentage of loans form census tract with high FICO
scores, FICO scores from top quartiles
27
Panel B. Financial variables from Bankscope for bank lenders
Variable name Bankscope data Description of variables (in Mill)
Tasset (in $bill) data5670 Total Assets (in Bill)
Totalcap data2135 Total capital reserve
Tier1cap data2130 Total tier one capital
Equity data2055 Total common equity (in Bill)
Liability (in $bill) data2060 - data2055 Total liability(in Bill)
Total earning assets relative to total
Earningasset/liability data2010 / liability
liabilities
Total off balance sheet items relative to total
OBS_cap data2065
capital
Liqat data2075 Total liquid assets
NI data2115 Net income
Profit data2105 Profit
NI_liab data2115/data5670 Net income relative to total liabilities
Totalloans data5330 Total Loans
TotalfixAt data5660 Total fixed assets
Tdeposit_loan data6080/data5330 Total deposits relative to total loans
Tprobloan_cap data2150/data2135 Total problem loans relative to capital
Tprobloans_liqAt data5240/ data2075 Total problem loans relative to liquid assets
Loanres_loan data2070/ data5330 Total loan reserves relative to total loans
(data2130-
ChngT1ratio Percentage change in tier one capital reserve
lag(data2130))/lag(data2130)
(data2140-
ChngTotalcap Percentage change in total capital reserve
lag(data2140))/lag(data2140)
ChngLiqAT (data2075-
Percentage change in total liquid assets
lag(data2075))/lag(data2075)
(data2055- lag(data2055))
ChngEq Percentage change in total common equity
/lag(data2055)
(data2065- Percentage change in off balance sheet
ChngOBS
lag(data2065))/lag(data2135) assets relative to total capital
ChngProfit_cap (data2105- Average change profit relative to common
lag(data2105))/lag(data2135) equity
(data2115- Average change net income relative to total
ChngNI_cap
lag(data2115))/lag(data2135) loans
(data2070- lag(data2070))/lag( Average change in total loan reserves
ChngLoanRes_loans
data5330) relative to total capital
ChngTasset (data5670- Average change in total assets scaled by
lag(data5670))/lag(data5670) lagged total assets
(data5330- Average change in total loans relative to
Chngtotaloans_cap
lag(data5330))/lag(data2135) total capital
(data2150- Average change in total problem loans
ChngTprobloans_cap lag(data2150))/lag(data2135) relative to total capital
28
Table 2. Summary statistics of mortgage lending activities for failed bank and non-banks.
The detailed description of the variables are shown in Panel A of Table 1.
Panel A. Failed banks
Variables N MIN MAX MEAN STD
Application 249 4.500 2431891.500 35135.071 162639.856
Pctoriginate 249 0.000 1.000 0.494 0.232
Loan-to-income 248 0.638 86.021 2.789 5.385
Pctminority 249 0.000 1.000 0.519 0.204
Pctgoodfico 249 0.000 0.800 0.222 0.127
Avgamount (‘000) 249 33.312 9573.753 220.134 601.006
Amount (Bill) 249 0.004 4732.701 63.792 313.734
Pctpurchase 249 0.000 1.000 0.404 0.254
Pctrefinance 249 0.000 1.000 0.554 0.232
HHI 249 235.556 1533.397 350.721 108.666
High_HPI 249 0.000 1.000 0.525 0.476
Concentration 247 414.114 10000.000 4882.047 3523.814
Highrate 249 0.000 9.000 3.180 2.033
HOEPA 249 0.000 8.667 1.167 1.597
Chngamount (in mill) 223 -8.807 171.265 2.710 12.485
Chngloanorig 223 -35538.000 276605.333 5096.777 22026.970
Chngpctapproval 223 -0.972 0.747 -0.070 0.178
Chngpctminority 223 -0.556 0.914 0.057 0.171
ChnggoodFICO 223 -0.667 0.433 -0.017 0.116
Panel B. Failed non-banks
Variables N MIN MAX MEAN STD
Application 329 1.500 2431891.500 27142.123 168356.432
Pctoriginate 329 0.045 1.000 0.656 0.252
Loan-to-income 327 0.331 24.961 2.239 1.709
Pctminority 329 0.000 1.000 0.393 0.226
PctgoodFICO 329 0.000 0.922 0.242 0.186
Avgamount (‘000) 329 33.312 35004.445 381.216 1986.979
Amount (Bill) 329 0.003 4732.701 53.354 340.129
Pctpurchase 329 0.000 1.000 0.412 0.231
Pctrefinance 329 0.000 1.000 0.467 0.221
HHI 329 222.196 1967.938 389.288 167.534
High_HPI 329 0.000 1.000 0.330 0.460
Concentration 329 397.048 10000.000 7992.232 2860.919
Highrate 329 0.000 9.000 2.779 2.057
HOEPA 329 0.000 7.000 0.784 1.370
Chngamount (in mill) 308 -2.402 171.265 2.239 13.142
Chngloanorig 308 -5623.333 276605.333 4392.027 24855.874
Chngpctapproval 308 -0.939 0.747 -0.028 0.149
Chngpctminority 308 -0.750 0.889 0.036 0.179
ChnggoodFICO 308 -0.667 0.500 -0.013 0.141
29
Table 3. Summary statistics of HMDA variables for match (non-failed) banks and non-banks
The detailed variable description is in Panel A of Table 1.
Non-failed (active) banks Non-failed (still active) non-banks
Variables N MIN MAX MEAN STD N MIN MAX MEAN STD
Application 2240 1.000 146091.75 816.835 4685.682 3215 1.000 1287758. 2749.048 31748.302
Pctoriginate 2240 0.006 1.000 0.791 0.147 3215 0.000 1.000 0.751 0.224
Loan-to-income 2238 0.141 31.374 1.757 1.068 3209 0.112 29.151 1.951 1.040
Pctminority 2240 0.000 1.000 0.242 0.206 3215 0.000 1.000 0.360 0.250
PctgoodFICO 2240 0.000 1.000 0.303 0.285 3215 0.000 1.000 0.259 0.218
Avgamount (‘000) 2240 11.406 4694.989 164.942 205.832 3215 5.317 15982.78 148.327 365.562
Amount (bill) 2240 0.000 13.005 0.109 0.566 3215 0.000 207.055 0.446 5.098
Pctpurchase 2240 0.000 1.000 0.424 0.155 3215 0.000 1.000 0.358 0.269
Pctrefinance 2240 0.000 0.953 0.403 0.146 3215 0.000 1.000 0.467 0.231
HHI 2240 0.000 2099.517 416.636 187.489 3215 0.000 2505.969 359.056 136.657
High_HPI 2240 0.000 1.000 0.204 0.398 3215 0.000 1.000 0.381 0.476
Concentration 2240 580.954 10000.000 9008.233 1543.376 3154 358.374 10000.00 8311.468 2362.765
Highrate 2240 0.000 9.000 2.449 1.865 3215 0.000 9.000 1.605 1.828
HOEPA 2240 0.000 7.667 0.468 1.041 3215 0.000 8.000 0.253 0.764
Chngamount (in mill) 2159 -75.027 5.602 -0.128 2.385 2914 -82.235 31.920 -0.263 3.943
Chngloanorig 2159 -142237.333 14902.333 -224.284 4005.847 2914 -27501.000 79202.33 -593.505 8888.673
Chngpctapproval 2159 -0.859 0.786 0.004 0.104 2914 -0.929 1.000 0.001 0.163
Chngpctminority 2159 -0.925 0.938 -0.012 0.134 2914 -1.000 1.000 0.007 0.164
ChnggoodFICO 2159 -0.979 0.862 0.009 0.147 2914 -0.881 0.731 -0.001 0.133
30
Table 4. Summary statistics of balance sheet information for failed and non-failed banks
The detailed discretion of the variables is shown in Panel B of Table1.
Failed (inactive) banks Non-failed (active) banks
Variables N MIN MAX MEAN STD N MIN MAX MEAN STD
Tasset (in bill) 267 0.000 0.485 0.009 0.053 2240 0.000 0.088 0.001 0.004
Totalcap (in mill) 264 1.645 48683.388 890.537 5281.968 2238 1.930 10474.928 85.771 462.418
Tier1cap (in mill) 266 4.975 477.071 14.588 31.600 2238 7.153 86.675 14.755 7.010
Equity (in mill) 267 1.916 56778.500 998.658 6027.316 2240 2.270 8425.375 93.490 499.440
Liability (in bill) 267 0.000 0.431 0.008 0.047 2240 0.000 0.080 0.001 0.004
Earningasset_liab 267 0.788 50.110 1.215 3.017 2240 0.690 1.932 1.021 0.054
OBS_cap 263 -27.182 41.486 2.077 3.905 2238 0.000 262.189 1.555 6.056
liqAT 267 1.781 60515.500 855.240 5277.740 2240 0.911 21979.375 55.796 523.882
NI (in mill) 267 -150.924 6896.042 91.790 653.583 2240 -101.196 781.654 6.972 40.075
Profit (in mill) 267 -116.029 9177.000 131.147 879.833 2240 -126.889 1166.354 10.225 59.493
NI_liab 267 -0.052 36.541 0.138 2.236 2240 -0.081 0.381 0.009 0.011
Totaloans 267 0.000 319395.917 5774.676 33285.149 2240 6.866 64732.829 574.229 2855.614
TotalfixAt 267 0.000 6081.938 98.650 592.719 2240 0.039 947.683 11.421 43.855
Tdeposit_loanratio 266 -0.384 3.893 1.144 0.367 2239 0.485 93.037 1.373 2.023
Tprobloan_cap 264 -8.033 42.639 0.229 2.674 2238 -0.010 1.776 0.026 0.058
Tprobloans_liqAt 267 0.000 4.639 0.475 0.554 2240 0.000 10.198 0.191 0.331
Loanres_loan 266 -0.140 0.087 0.016 0.012 2240 0.001 0.111 0.013 0.006
ChngT1ratio 262 -0.947 6.491 -0.033 0.524 2238 -7.671 5.172 0.023 0.275
ChngTotalcap 262 -0.938 68.465 0.725 4.643 2238 -4.672 91.399 0.426 2.485
ChngLiqAT 265 -0.659 69.314 1.727 5.646 2240 -0.973 449.640 1.274 14.296
ChngEq 265 -1.872 68.407 0.762 4.775 2240 -5.934 99.540 0.526 3.253
ChngOBS 259 -1.000 411.263 7.582 37.780 2237 -1.000 2099.727 4.789 54.596
ChngProfit_cap 264 -335.640 17.786 -1.156 20.700 2239 -3.342 6.478 0.074 0.369
ChngNI_cap 264 -220.990 10.959 -0.779 13.626 2239 -3.295 3.820 0.052 0.247
ChngLoanRes_loan 264 -0.062 0.275 0.009 0.025 2240 -0.043 48.866 0.053 1.292
ChngTasset 265 -0.856 146.488 1.059 9.070 2240 -0.974 234.780 0.705 5.877
Chngtotaloans_cap 264 -6.866 238.246 5.564 22.086 2239 -13.993 733.508 3.080 18.880
ChngTprobloans_cap 263 -0.601 2.478 0.047 0.190 2239 -1.526 3.845 0.006 0.123
31
Table 5. Probability of failure analysis of bank and non-bank lenders
The dependent variable failure takes on the value 1 for lenders that failed by August 2010, zero otherwise. The
explanatory variables capture the time series average lending practices based on HMDA data for 2003 to 2007.
Pctoriginate is the ratio of origination relative to loan application in percentage. Logamount is the natural
logarithm of the average loan amount. Pctorg*Logamount is an interaction variable of Pctoriginate and
Logamount variables. PctgoodFICO is the percentage of total loan application from census tracts with Average
FICO scores above the median. Pctminority is the percentage of loan applications by minorities. Pctrefinance is
the percentage of loan application for refinance purposes. Loan-to-income is the average loan-to-income ratio
for all loans by lenders. HighComp is a dummy variable that takes on the value 1 for lenders operating in highly
competitive environment, where the Concentration measure is below the median. Highrate is the annual average
of total number of spread reportable loans. High_HPI is the dummy variable which takes on the value one if
more than half of the originations are in areas with high property prices. The 1 percent, 5 percent and 10 percent
significance levels are denoted with ***, **, and *, respectively. To save space the intercepts are not shown.
Non-banks Banks
odds ratio odds ratio odds ratio odds ratio
(1) (2) (1) (2)
Pctoriginate 0.918* 0.918** 1.067 1.068
(-1.859) (-1.987) (1.401) (1.412)
Logamount 1.158 1.143 1.827** 1.800**
(0.937) (0.941) (2.365) (2.293)
Pctorg*Logamount 1.005 1.005 0.993* 0.993*
(1.388) (1.452) (-1.664) (-1.673)
PctgoodFICO 0.985 0.986 0.987* 0.988*
(-1.466) (-1.262) (-1.924) (-1.928)
Pctminority 0.998 1.000 1.018*** 1.018***
(-0.477) (0.026) (5.186) (4.223)
Pctrefinance 1.010*** 1.010*** 1.013 1.014
(4.012) (4.435) (1.413) (1.514)
Loan-to-income 1.001 0.999 1.175*** 1.179***
(0.162) (-0.265) (3.607) (3.666)
Focus 0.812*** 0.794*** 0.807 0.800
(-2.897) (-2.819) (-1.228) (-1.295)
HighComp 1.160 1.214* 1.356 1.383
(1.355) (1.656) (1.569) (1.552)
Highrate 1.235*** 1.075***
(4.577) (2.583)
High_HPI 1.026 1.190
(0.671) (1.166)
Observations 3394 3394 2562 2562
pseudo R-squared 0.390 0.404 0.182 0.184
32
Table 6. Probability of failure analysis of bank and non-bank lenders with time trends
The dependent variable failure takes on the value 1 for lenders that failed by August 2010, zero otherwise. The
explanatory variables capture the time series average lending practices based on HMDA data for 2003 to 2007.
Pctoriginate is the ratio of origination relative to loan application in percentage. Logamount is the natural
logarithm of the average loan amount. Pctorg*Logamount is an interaction variable of Pctoriginate and
Logamount variables. PctgoodFICO is the percentage of total loan application from census tracts with Average
FICO scores above the median. Pctminority is the percentage of loan applications by minorities. Pctrefinance is
the percentage of loan application for refinance purposes. Loan-to-income is the average loan-to-income ratio
for all loans by lenders. HighComp is a dummy variable that takes on the value 1 for lenders operating in highly
competitive environment, where the Concentration measure is below the median. Highrate is the annual average
of total number of spread reportable loans. High_HPI is the dummy variable which takes on the value one if
more than half of the originations are in areas with high property prices. Chngloanamount is the average
annual change in the total loan amount by the lender from 2003 to 2007. Chngpctapproval is the average annual
percentage change in approval rates. Robust z statistics are reported in parentheses. The 1 percent, 5 percent and
10 percent significance levels are denoted with ***, **, and *, respectively. To save space the intercepts are not
shown.
Non-banks Banks
odds ratio odds ratio odds ratio odds ratio
(1) (2) (1) (2)
Pctoriginate 0.891** 0.878** 1.069 1.056
(-2.434) (-2.453) -1.483 (1.144)
Logamount 0.973 0.942 1.796** 1.732**
(-0.157) (-0.302) -2.37 (2.233)
Pctorg*Logamount 1.007* 1.008* 0.993* 0.994
-1.952 (1.900) (-1.728) (-1.571)
PctgoodFICO 0.977*** 0.977*** 0.986** 0.986**
(-3.841) (-3.907) (-2.096) (-2.188)
Pctminority 0.998 0.998 1.017*** 1.017***
(-0.374) (-0.518) -3.396 (3.512)
Pctrefinance 1.012*** 1.012*** 1.013 1.012
-8.062 (8.188) -1.214 (1.292)
Loan-to-income 1.004* 1.003 1.158*** 1.154***
-1.892 (1.245) -3.096 (2.998)
Focus 0.770*** 0.755*** 0.775* 0.739*
(-5.142) (-6.201) (-1.803) (-1.936)
HighComp 1.211 1.310 1.35 1.363
-1.29 (1.344) -1.534 (1.472)
Highrate 1.202*** 1.183*** 1.045 1.044
-4.845 (4.083) -1.358 (1.338)
High_HPI 0.997 1.028 1.094 1.087
(-0.027) (0.290) -0.581 (0.548)
Chngloanamount 1.038 1.053 1.000*** 1.000***
-1.393 (1.531) -3.465 (3.695)
Chngpctapproval 5.963*** 6.382**
(6.129) (2.397)
Observations 3086 3086 2461 2461
pseudo R-squared 0.437 0.444 0.186 0.193
33
Table 7. Probability of failure analysis of bank lenders with financial information
The dependent variable is the failure dummy in a logistic regression. The lending characteristics variables are
defined in Tables 5 and 6. Tier1capratio is the total tier1 capital ratio in percentage. NI_asset is the ratio of Net
income to earning assets. Probloanratio is the ratio of total problem loans to total loans. Depratio is the ratio of
deposits to total assets for the bank. LogtotalfixAt is the natural logarithm of total fixed assets. Tprobloan_cap is
the total problem loans relative to the total capital reserve. OBS_cap is the total off balance sheet items relative
to total capital reserve. Loanres_loan is the total loan reserves relative to the total loans. Tprobloans_liqAt is the
total problem loans relative to total liquid assets. Robust z-statistics are reported in parentheses. The 1 percent, 5
percent and 10 percent significance levels are denoted with ***, **, and *, respectively. To save space the
intercepts are not shown.
Odds ratio Odds ratio Odds ratio Odds ratio Odds ratio
(1) (2) (3) (4) (5)
Pctoriginate 1.089*** 1.131*** 1.124*** 1.134*** 1.123***
(2.659) (5.446) (3.597) (4.406) (4.504)
Logamount 1.964*** 2.782*** 2.875*** 3.112*** 2.590***
(5.934) (15.263) (6.458) (10.516) (9.993)
Pctorg*Logamount 0.992*** 0.988*** 0.989*** 0.988*** 0.989***
(-2.811) (-5.203) (-3.488) (-4.227) (-4.312)
PctgoodFICO 0.989*** 0.988*** 0.986*** 0.988*** 0.986***
(-2.663) (-2.966) (-3.981) (-4.347) (-5.382)
Pctminority 1.018*** 1.010** 1.015*** 1.014*** 1.013***
(4.413) (2.569) (3.771) (3.402) (3.284)
Pctrefinance 1.008 1.017*** 1.012* 1.012** 1.013**
(1.568) (2.685) (1.821) (2.145) (2.300)
Loan-to-income 1.156*** 1.068** 1.097** 1.088** 1.098***
(2.916) (2.455) (2.205) (2.288) (2.596)
High_HPI 1.307*** 1.398** 1.246* 1.339*** 1.280***
(2.595) (2.484) (1.696) (3.350) (2.816)
Tier1capratio 0.892* 0.890*** 0.857*** 0.865*** 0.869***
(-1.934) (-2.780) (-2.921) (-3.536) (-3.191)
NI_asset 0.539*** 0.389*** 0.432*** 0.428***
(-6.301) (-7.715) (-7.915) (-8.137)
Deposit_ratio 0.985***
(-2.716)
Probloanratio 1.728***
(10.655)
LogtotalfixAt 1.057
(0.420)
Tprobloan_cap 1.266*** 1.262*** 1.048
(2.881) (4.650) (0.948)
OBS_cap 1.042**
(2.125)
Loanres_loan 2.052*** 1.827***
(7.505) (5.954)
Tprobloans_liqAt 3.139***
(4.819)
Observations 2487 2487 2451 2452 2452
pseudo R-squared 0.147 0.328 0.261 0.282 0.295
34
Appendix – Table 1
The extract shows some of the largest bank and non-bank lenders as of 2007, and the amount of high cost loans
that these institutions issued. The assets for Banks (depository institutions) are reported in million USD, while
the assets for non-banks reflect the minimum asset requirement (defined by the states).
* reflects subprime lenders with at least one depository facility therefore considered banks in this classification,
although these institutions are widely viewed as non-banks.
Non-banks High-cost loans Asset Banks High-cost Loans Asset
New Century Mortgage Corp. 193,04 10,000 Countrywide Home Loans * 210,688 175.09
WMC Mortgage Co. 142,16 31,000 National City Bank 189,415 69.48
Option One Mortgage Corp 118,43 10,000 Fremont Investment & Loan 140,290 11.32
Argent Mortgage Co. 107,53 10,000 Wells Fargo Bank, NA 110,770 403.26
American Home Mortgage 78,936 10,000 Long Beach Mortgage Co. 95,427 330.71
Accredited Home Lenders, 75,931 10,000 Decision One Mortgage 73,379 3.39
Homecoming Financial 62,730 10,000 Beneficial Company LLC 67,303 4.02
Novastar Mortgage, Inc. 43,733 10,000 Indymac Bank, FSB* 62,790 20.33
Wilmington Finance, Inc. 41,212 10,000 Equifirst Corp.* 62,685 81.07
35
Appendix – Table 2
FailD takes on the value one for lenders that fail (closed down, merged, or acquired) by August 2010, zero otherwise. Pctoriginate is the ratio of origination relative to loan
application in percentage. Logamount is the natural logarithm of the average loan amount. Orig*Amount is an interaction variable of Pctoriginate and Logamount variables.
PctgoodFICO is the percentage of total loan application from census tracts with Average FICO scores above the median. Pctminority is the percentage of loan applications by
minorities. Pctrefinance is the percentage of loan application for refinance purposes. Loan-to-income is the average loan-to-income ratio for all loans by the specific lender.
HighComp is a dummy variable that takes on the value 1 for lenders operating in highly competitive environment, where the Concentration measure is below the median.
Highrate is the annual average of total number of spread reportable loans. High_HPI is the dummy variable which takes on the value one if more than half of the originations
are in areas with high property prices. The pair-wise correlation coefficient estimates are reported with the corresponding p-values in italic.
Panel A. Correlation analysis results for non-bank residential lenders
Orig* Loan-to-
Faild Pctoriginate Logamount amount PctgoodFICO Pctminority Pctrefinance income Focus HighComp Highrate High_HPI
Faild 1.000
Pctoriginate -0.283 1.000
0.000
Logamount 0.405 -0.359 1.000
0.000 0.000
Orig*amount -0.088 0.782 0.253 1.000
0.000 0.000 0.000
PctgoodFICO -0.045 0.194 0.098 0.272 1.000
0.008 0.000 0.000 0.000
Pctminority 0.164 -0.325 0.244 -0.201 -0.241 1.000
0.000 0.000 0.000 0.000 0.000
Pctrefinance 0.097 -0.025 0.054 -0.034 0.068 -0.003 1.000
0.000 0.148 0.002 0.045 0.000 0.880
Loan-to- 0.122 -0.195 0.272 -0.027 -0.001 0.179 0.069 1.000
income 0.000 0.000 0.000 0.110 0.946 0.000 0.000
Focus -0.377 0.323 -0.551 0.014 0.021 -0.199 -0.068 -0.141 1.000
0.000 0.000 0.000 0.420 0.230 0.000 0.000 0.000
HighComp -0.068 0.049 -0.106 0.012 0.046 -0.044 -0.014 -0.018 0.101 1.000
0.000 0.004 0.000 0.479 0.007 0.010 0.429 0.289 0.000
Highrate 0.216 -0.072 0.360 0.146 -0.024 0.043 -0.027 0.071 -0.177 -0.081 1.000
0.000 0.000 0.000 0.000 0.167 0.012 0.113 0.000 0.000 0.000
High_HPI 0.078 -0.199 0.198 -0.055 0.007 0.386 0.060 0.187 -0.045 0.072 0.028 1.000
0.000 0.000 0.000 0.001 0.667 0.000 0.000 0.000 0.008 0.000 0.102
36
Panel B. Correlation analysis results for bank residential lenders
Orig* Loan-to-
Faild Pctoriginate Logamount amount PctgoodFICO Pctminority pctrefinance income Focus Highcomp Highrate High_HPI
Faild 1.000
Pctoriginate -0.266 1.000
0.000
Logamount 0.276 -0.371 1.000
0.000 0.000
Orig*amount -0.116 0.737 0.323 1.000
0.000 0.000 0.000
PctgoodFICO -0.074 0.131 0.110 0.208 1.000
0.000 0.000 0.000 0.000
Pctminority 0.234 -0.298 0.120 -0.233 -0.153 1.000
0.000 0.000 0.000 0.000 0.000
Pctrefinance 0.135 -0.112 0.335 0.096 0.226 -0.170 1.000
0.000 0.000 0.000 0.000 0.000 0.000
Loan-to- 0.136 -0.075 0.160 0.045 0.141 0.214 0.092 1.000
income 0.000 0.000 0.000 0.022 0.000 0.000 0.000
Focus -0.221 0.249 -0.448 -0.010 0.010 -0.183 -0.073 -0.042 1.000
0.000 0.000 0.000 0.619 0.622 0.000 0.000 0.032
HighComp -0.029 0.052 -0.079 0.007 0.135 -0.031 0.037 0.030 0.055 1.000
0.138 0.008 0.000 0.708 0.000 0.115 0.062 0.134 0.005
Highrate 0.058 -0.055 0.113 0.018 -0.207 -0.137 0.015 -0.111 -0.069 -0.172 1.000
0.003 0.005 0.000 0.360 0.000 0.000 0.442 0.000 0.000 0.000
High_HPI 0.103 -0.164 0.114 -0.083 0.038 0.334 -0.064 0.163 -0.041 0.054 -0.154 1.000
0.000 0.000 0.000 0.000 0.057 0.000 0.001 0.000 0.040 0.007 0.000
37